# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import Counter
from typing import Any, Dict, List, Optional, Union

import torch
from typing_extensions import Literal

from lightning_lite.accelerators import ACCELERATOR_REGISTRY
from lightning_lite.accelerators.accelerator import Accelerator
from lightning_lite.accelerators.cuda import CUDAAccelerator
from lightning_lite.accelerators.mps import MPSAccelerator
from lightning_lite.accelerators.tpu import TPUAccelerator
from lightning_lite.plugins import (
    CheckpointIO,
    DeepSpeedPrecision,
    NativeMixedPrecision,
    Precision,
    TPUBf16Precision,
    TPUPrecision,
)
from lightning_lite.plugins.environments import (
    ClusterEnvironment,
    KubeflowEnvironment,
    LightningEnvironment,
    LSFEnvironment,
    SLURMEnvironment,
    TorchElasticEnvironment,
)
from lightning_lite.plugins.precision.double import DoublePrecision
from lightning_lite.strategies import (
    DDPShardedStrategy,
    DDPSpawnShardedStrategy,
    DDPSpawnStrategy,
    DDPStrategy,
    DeepSpeedStrategy,
    SingleDeviceStrategy,
    SingleTPUStrategy,
    Strategy,
    STRATEGY_REGISTRY,
    XLAStrategy,
)
from lightning_lite.strategies.ddp_spawn import _DDP_FORK_ALIASES
from lightning_lite.utilities import _StrategyType, rank_zero_info, rank_zero_warn
from lightning_lite.utilities.device_parser import _determine_root_gpu_device
from lightning_lite.utilities.imports import _IS_INTERACTIVE

_PLUGIN = Union[Precision, ClusterEnvironment, CheckpointIO]
_PLUGIN_INPUT = Union[_PLUGIN, str]
_PRECISION_INPUT = Literal[16, 32, 64, "bf16"]


class _Connector:
    """The Connector parses several Lite arguments and instantiates the Strategy including its owned components.

        A. accelerator flag could be:
            1. accelerator class
            2. accelerator str
            3. accelerator auto

        B. strategy flag could be:
            1. strategy class
            2. strategy str registered with STRATEGY_REGISTRY
            3. strategy str in _strategy_type enum which listed in each strategy as
               backend (registed these too, and _strategy_type could be deprecated)

        C. plugins flag could be:
            1. List of str, which could contain:
                i. precision str (Not supported in the old accelerator_connector version)
                ii. checkpoint_io str (Not supported in the old accelerator_connector version)
                iii. cluster_environment str (Not supported in the old accelerator_connector version)
            2. List of class, which could contains:
                i. precision class (should be removed, and precision flag should allow user pass classes)
                ii. checkpoint_io class
                iii. cluster_environment class


    priorities which to take when:
        A. Class > str
        B. Strategy > Accelerator/precision/plugins
    """

    def __init__(
        self,
        accelerator: Optional[Union[str, Accelerator]] = None,
        strategy: Optional[Union[str, Strategy]] = None,
        devices: Optional[Union[List[int], str, int]] = None,
        num_nodes: int = 1,
        precision: _PRECISION_INPUT = 32,
        plugins: Optional[Union[_PLUGIN_INPUT, List[_PLUGIN_INPUT]]] = None,
    ) -> None:

        # These arguments can be set through environment variables set by the CLI
        accelerator = self._argument_from_env("accelerator", accelerator, default=None)
        strategy = self._argument_from_env("strategy", strategy, default=None)
        devices = self._argument_from_env("devices", devices, default=None)
        num_nodes = self._argument_from_env("num_nodes", num_nodes, default=1)
        precision = self._argument_from_env("precision", precision, default=32)

        # 1. Parsing flags
        # Get registered strategies, built-in accelerators and precision plugins
        self._registered_strategies = STRATEGY_REGISTRY.available_strategies()
        self._registered_accelerators = ACCELERATOR_REGISTRY.available_accelerators()
        self._precision_types = ("16", "32", "64", "bf16")

        # Raise an exception if there are conflicts between flags
        # Set each valid flag to `self._x_flag` after validation
        # For devices: Assign gpus, etc. to the accelerator flag and devices flag
        self._strategy_flag: Optional[Union[Strategy, str]] = None
        self._accelerator_flag: Optional[Union[Accelerator, str]] = None
        self._precision_input: Optional[_PRECISION_INPUT] = None
        self._precision_instance: Optional[Precision] = None
        self._cluster_environment_flag: Optional[Union[ClusterEnvironment, str]] = None
        self._parallel_devices: List[Union[int, torch.device, str]] = []
        self.checkpoint_io: Optional[CheckpointIO] = None

        self._check_config_and_set_final_flags(
            strategy=strategy,
            accelerator=accelerator,
            precision=precision,
            plugins=plugins,
        )
        self._check_device_config_and_set_final_flags(devices=devices, num_nodes=num_nodes)

        # 2. Instantiate Accelerator
        # handle `auto`, `None` and `gpu`
        if self._accelerator_flag == "auto" or self._accelerator_flag is None:
            self._accelerator_flag = self._choose_auto_accelerator()
        elif self._accelerator_flag == "gpu":
            self._accelerator_flag = self._choose_gpu_accelerator_backend()

        self._set_parallel_devices_and_init_accelerator()

        # 3. Instantiate ClusterEnvironment
        self.cluster_environment: ClusterEnvironment = self._choose_and_init_cluster_environment()

        # 4. Instantiate Strategy - Part 1
        if self._strategy_flag is None:
            self._strategy_flag = self._choose_strategy()
        # In specific cases, ignore user selection and fall back to a different strategy
        self._check_strategy_and_fallback()
        self._init_strategy()

        # 5. Instantiate Precision Plugin
        self.precision = self._check_and_init_precision()

        # 6. Instantiate Strategy - Part 2
        self._lazy_init_strategy()

    def _check_config_and_set_final_flags(
        self,
        strategy: Optional[Union[str, Strategy]],
        accelerator: Optional[Union[str, Accelerator]],
        precision: _PRECISION_INPUT,
        plugins: Optional[Union[_PLUGIN_INPUT, List[_PLUGIN_INPUT]]],
    ) -> None:
        """This method checks:

        1. strategy: whether the strategy name is valid, and sets the internal flags if it is.
        2. accelerator: if the value of the accelerator argument is a type of accelerator (instance or string),
            set self._accelerator_flag accordingly.
        3. precision: The final value of the precision flag may be determined either by the precision argument or
            by a plugin instance.
        4. plugins: The list of plugins may contain a Precision plugin, CheckpointIO, ClusterEnvironment and others.
            Additionally, other flags such as `precision` can populate the list with the
            corresponding plugin instances.
        """
        if plugins is not None:
            plugins = [plugins] if not isinstance(plugins, list) else plugins

        if isinstance(strategy, str):
            strategy = strategy.lower()

        if strategy is not None:
            self._strategy_flag = strategy

        if strategy is not None and strategy not in self._registered_strategies and not isinstance(strategy, Strategy):
            raise ValueError(
                f"You selected an invalid strategy name: `strategy={strategy!r}`."
                " Example choices: ddp, ddp_spawn, deepspeed, dp, ..."
                " Find a complete list of options in our documentation at https://lightning.ai"
            )

        if (
            accelerator is not None
            and accelerator not in self._registered_accelerators
            and accelerator not in ("auto", "gpu")
            and not isinstance(accelerator, Accelerator)
        ):
            raise ValueError(
                f"You selected an invalid accelerator name: `accelerator={accelerator!r}`."
                f" Available names are: {', '.join(self._registered_accelerators)}."
            )

        self._accelerator_flag = accelerator

        if precision is not None:
            if str(precision) not in self._precision_types:
                raise ValueError(
                    f"Precision {repr(precision)} is invalid. Allowed precision values: {self._precision_types}"
                )
            self._precision_input = precision

        if plugins:
            plugins_flags_types: Dict[str, int] = Counter()
            for plugin in plugins:
                if isinstance(plugin, Precision):
                    self._precision_instance = plugin
                    plugins_flags_types[Precision.__name__] += 1
                elif isinstance(plugin, CheckpointIO):
                    self.checkpoint_io = plugin
                    plugins_flags_types[CheckpointIO.__name__] += 1
                elif isinstance(plugin, ClusterEnvironment):
                    self._cluster_environment_flag = plugin
                    plugins_flags_types[ClusterEnvironment.__name__] += 1
                else:
                    raise TypeError(
                        f"Found invalid type for plugin {plugin}. Expected one of: Precision, "
                        "CheckpointIO, ClusterEnviroment."
                    )

            duplicated_plugin_key = [k for k, v in plugins_flags_types.items() if v > 1]
            if duplicated_plugin_key:
                raise ValueError(
                    f"Received multiple values for {', '.join(duplicated_plugin_key)} flags in `plugins`."
                    " Expected one value for each type at most."
                )

        # handle the case when the user passes in a strategy instance which has an accelerator, precision,
        # checkpoint io or cluster env set up
        # TODO: improve the error messages below
        if self._strategy_flag and isinstance(self._strategy_flag, Strategy):
            if self._strategy_flag._accelerator:
                if self._accelerator_flag:
                    raise ValueError("accelerator set through both strategy class and accelerator flag, choose one")
                else:
                    self._accelerator_flag = self._strategy_flag._accelerator
            if self._strategy_flag._precision:
                # [RFC] handle precision plugin set up conflict?
                if self._precision_instance:
                    raise ValueError("precision set through both strategy class and plugins, choose one")
                else:
                    self._precision_instance = self._strategy_flag._precision
            if self._strategy_flag._checkpoint_io:
                if self.checkpoint_io:
                    raise ValueError("checkpoint_io set through both strategy class and plugins, choose one")
                else:
                    self.checkpoint_io = self._strategy_flag._checkpoint_io
            if getattr(self._strategy_flag, "cluster_environment", None):
                if self._cluster_environment_flag:
                    raise ValueError("cluster_environment set through both strategy class and plugins, choose one")
                else:
                    self._cluster_environment_flag = getattr(self._strategy_flag, "cluster_environment")

            if hasattr(self._strategy_flag, "parallel_devices"):
                if self._strategy_flag.parallel_devices:
                    if self._strategy_flag.parallel_devices[0].type == "cpu":
                        if self._accelerator_flag and self._accelerator_flag not in ("auto", "cpu"):
                            raise ValueError(
                                f"CPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                                f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                            )
                        self._accelerator_flag = "cpu"
                    if self._strategy_flag.parallel_devices[0].type == "cuda":
                        if self._accelerator_flag and self._accelerator_flag not in ("auto", "cuda", "gpu"):
                            raise ValueError(
                                f"GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                                f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                            )
                        self._accelerator_flag = "cuda"
                    self._parallel_devices = self._strategy_flag.parallel_devices

    def _check_device_config_and_set_final_flags(
        self, devices: Optional[Union[List[int], str, int]], num_nodes: int
    ) -> None:
        self._num_nodes_flag = int(num_nodes) if num_nodes is not None else 1
        self._devices_flag = devices

        if self._devices_flag in ([], 0, "0"):
            accelerator_name = (
                self._accelerator_flag.__class__.__qualname__
                if isinstance(self._accelerator_flag, Accelerator)
                else self._accelerator_flag
            )
            raise ValueError(
                f"`Lite(devices={self._devices_flag!r})` value is not a valid input"
                f" using {accelerator_name} accelerator."
            )

        if self._devices_flag == "auto" and self._accelerator_flag is None:
            raise ValueError(
                f"You passed `devices={devices}` but haven't specified"
                " `accelerator=('auto'|'tpu'|'gpu'|'cpu'|'mps')` for the devices mapping."
            )

    def _choose_auto_accelerator(self) -> str:
        """Choose the accelerator type (str) based on availability when ``accelerator='auto'``."""
        if self._accelerator_flag == "auto":
            if TPUAccelerator.is_available():
                return "tpu"
            if MPSAccelerator.is_available():
                return "mps"
            if CUDAAccelerator.is_available():
                return "cuda"
        return "cpu"

    @staticmethod
    def _choose_gpu_accelerator_backend() -> str:
        if MPSAccelerator.is_available():
            return "mps"
        if CUDAAccelerator.is_available():
            return "cuda"

        raise RuntimeError("No supported gpu backend found!")

    def _set_parallel_devices_and_init_accelerator(self) -> None:
        if isinstance(self._accelerator_flag, Accelerator):
            self.accelerator: Accelerator = self._accelerator_flag
        else:
            assert self._accelerator_flag is not None
            self.accelerator = ACCELERATOR_REGISTRY.get(self._accelerator_flag)
        accelerator_cls = self.accelerator.__class__

        if not accelerator_cls.is_available():
            available_accelerator = [
                acc_str
                for acc_str in self._registered_accelerators
                if ACCELERATOR_REGISTRY[acc_str]["accelerator"].is_available()
            ]
            raise RuntimeError(
                f"`{accelerator_cls.__qualname__}` can not run on your system"
                " since the accelerator is not available. The following accelerator(s)"
                " is available and can be passed into `accelerator` argument of"
                f" `Lite`: {available_accelerator}."
            )

        self._set_devices_flag_if_auto_passed()

        self._devices_flag = accelerator_cls.parse_devices(self._devices_flag)
        if not self._parallel_devices:
            self._parallel_devices = accelerator_cls.get_parallel_devices(self._devices_flag)

    def _set_devices_flag_if_auto_passed(self) -> None:
        if self._devices_flag == "auto" or self._devices_flag is None:
            self._devices_flag = self.accelerator.auto_device_count()

    def _choose_and_init_cluster_environment(self) -> ClusterEnvironment:
        if isinstance(self._cluster_environment_flag, ClusterEnvironment):
            return self._cluster_environment_flag
        for env_type in (
            SLURMEnvironment,
            TorchElasticEnvironment,
            KubeflowEnvironment,
            LSFEnvironment,
        ):
            if env_type.detect():
                return env_type()
        return LightningEnvironment()

    def _choose_strategy(self) -> Union[Strategy, str]:
        if self._accelerator_flag == "tpu":
            if self._parallel_devices and len(self._parallel_devices) > 1:
                return "tpu_spawn"
            else:
                # TODO: lazy initialized device, then here could be self._strategy_flag = "single_tpu_device"
                return SingleTPUStrategy(device=self._parallel_devices[0])  # type: ignore
        if self._num_nodes_flag > 1:
            return "ddp"
        if len(self._parallel_devices) <= 1:
            # TODO: Change this once gpu accelerator was renamed to cuda accelerator
            if isinstance(self._accelerator_flag, (CUDAAccelerator, MPSAccelerator)) or (
                isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("cuda", "gpu", "mps")
            ):
                device = _determine_root_gpu_device(self._parallel_devices)
            else:
                device = "cpu"
            # TODO: lazy initialized device, then here could be self._strategy_flag = "single_device"
            return SingleDeviceStrategy(device=device)  # type: ignore
        if len(self._parallel_devices) > 1:
            if _IS_INTERACTIVE:
                return "ddp_fork"
            return "ddp_spawn"

        return "ddp"

    def _check_strategy_and_fallback(self) -> None:
        """Checks edge cases when the strategy selection was a string input, and we need to fall back to a
        different choice depending on other parameters or the environment."""
        # current fallback and check logic only apply to user pass in str config and object config
        # TODO this logic should apply to both str and object config
        strategy_flag = "" if isinstance(self._strategy_flag, Strategy) else self._strategy_flag

        if strategy_flag in ("ddp_spawn", "ddp_spawn_find_unused_parameters_false") and (
            TorchElasticEnvironment.detect()
            or KubeflowEnvironment.detect()
            or SLURMEnvironment.detect()
            or LSFEnvironment.detect()
        ):
            strategy_flag = "ddp"
        if strategy_flag == "dp" and self._accelerator_flag == "cpu":
            rank_zero_warn(f"{strategy_flag!r} is not supported on CPUs, hence setting `strategy='ddp'`.")
            strategy_flag = "ddp"
        if strategy_flag in _DDP_FORK_ALIASES and "fork" not in torch.multiprocessing.get_all_start_methods():
            raise ValueError(
                f"You selected `Lite(strategy='{strategy_flag}')` but process forking is not supported on this"
                f" platform. We recommed `Lite(strategy='ddp_spawn')` instead."
            )
        if strategy_flag:
            self._strategy_flag = strategy_flag

    def _init_strategy(self) -> None:
        """Instantiate the Strategy given depending on the setting of ``_strategy_flag``."""
        if isinstance(self._strategy_flag, str):
            self.strategy = STRATEGY_REGISTRY.get(self._strategy_flag)
        elif isinstance(self._strategy_flag, Strategy):
            self.strategy = self._strategy_flag
        else:
            raise RuntimeError(f"{self.strategy} is not valid type: {self.strategy}")

    def _check_and_init_precision(self) -> Precision:
        self._validate_precision_choice()
        if isinstance(self._precision_instance, Precision):
            return self._precision_instance

        if isinstance(self.accelerator, TPUAccelerator):
            if self._precision_input == 32:
                return TPUPrecision()
            elif self._precision_input in (16, "bf16"):
                if self._precision_input == 16:
                    rank_zero_warn(
                        "You passed `Lite(accelerator='tpu', precision=16)` but AMP"
                        " is not supported with TPUs. Using `precision='bf16'` instead."
                    )
                return TPUBf16Precision()
        if isinstance(self.strategy, DeepSpeedStrategy):
            return DeepSpeedPrecision(self._precision_input, amp_type="native", amp_level=None)  # type: ignore

        if self._precision_input == 32:
            return Precision()
        if self._precision_input == 64:
            return DoublePrecision()

        if self._precision_input == 16 and self._accelerator_flag == "cpu":
            rank_zero_warn(
                "You passed `Lite(accelerator='cpu', precision=16)` but native AMP is not supported on CPU."
                " Using `precision='bf16'` instead."
            )
            self._precision_input = "bf16"

        if self._precision_input in (16, "bf16"):
            rank_zero_info(
                "Using 16-bit Automatic Mixed Precision (AMP)"
                if self._precision_input == 16
                else "Using bfloat16 Automatic Mixed Precision (AMP)"
            )

            device = "cpu" if self._accelerator_flag == "cpu" else "cuda"
            return NativeMixedPrecision(self._precision_input, device)

        raise RuntimeError("No precision set")

    def _validate_precision_choice(self) -> None:
        """Validate the combination of choices for precision, and accelerator."""
        if isinstance(self.accelerator, TPUAccelerator):
            if self._precision_input == 64:
                raise NotImplementedError(
                    "`Lite(accelerator='tpu', precision=64)` is not implemented."
                    " Please, open an issue in `https://github.com/Lightning-AI/lightning/issues`"
                    " requesting this feature."
                )
            if self._precision_instance and not isinstance(self._precision_instance, (TPUPrecision, TPUBf16Precision)):
                raise ValueError(
                    f"The `TPUAccelerator` can only be used with a `TPUPrecision` plugin,"
                    f" found: {self._precision_instance}."
                )

    def _lazy_init_strategy(self) -> None:
        """Lazily set missing attributes on the previously instantiated strategy."""
        self.strategy.accelerator = self.accelerator
        if self.precision:
            self.strategy.precision = self.precision
        if self.checkpoint_io:
            self.strategy.checkpoint_io = self.checkpoint_io
        if hasattr(self.strategy, "cluster_environment"):
            self.strategy.cluster_environment = self.cluster_environment
        if hasattr(self.strategy, "parallel_devices"):
            if self.strategy.parallel_devices:
                self._parallel_devices = self.strategy.parallel_devices
            else:
                self.strategy.parallel_devices = self._parallel_devices
        if hasattr(self.strategy, "num_nodes"):
            self.strategy._num_nodes = self._num_nodes_flag
        if hasattr(self.strategy, "set_world_ranks"):
            self.strategy.set_world_ranks()
        self.strategy._configure_launcher()

        if _IS_INTERACTIVE and self.strategy.launcher and not self.strategy.launcher.is_interactive_compatible:
            raise RuntimeError(
                f"`Lite(strategy={self._strategy_flag!r})` is not compatible with an interactive"
                " environment. Run your code as a script, or choose one of the compatible strategies:"
                f" Lite(strategy=None|{'|'.join(_StrategyType.interactive_compatible_types())})."
                " In case you are spawning processes yourself, make sure to include the Lite"
                " creation inside the worker function."
            )

        # TODO: should be moved to _check_strategy_and_fallback().
        # Current test check precision first, so keep this check here to meet error order
        if isinstance(self.accelerator, TPUAccelerator) and not isinstance(
            self.strategy, (SingleTPUStrategy, XLAStrategy)
        ):
            raise ValueError(
                "The `TPUAccelerator` can only be used with a `SingleTPUStrategy` or `XLAStrategy`,"
                f" found {self.strategy.__class__.__name__}."
            )

    @staticmethod
    def _argument_from_env(name: str, current: Any, default: Any) -> Any:
        env_value: Optional[Union[str, int]] = os.environ.get("LT_" + name.upper())

        if env_value is None:
            return current

        if name == "precision":
            # TODO: support precision input as string, then this special handling is not needed
            env_value = int(env_value) if env_value in ("16", "32", "64") else env_value

        if env_value is not None and env_value != current and current != default:
            raise ValueError(
                f"Your code has `LightningLite({name}={current!r}, ...)` but it conflicts with the value "
                f"`--{name}={current}` set through the CLI. "
                " Remove it either from the CLI or from the Lightning Lite object."
            )
        if env_value is None:
            return current
        return env_value

    @property
    def is_distributed(self) -> bool:
        # TODO: deprecate this property
        # Used for custom plugins.
        # Custom plugins should implement is_distributed property.
        if hasattr(self.strategy, "is_distributed") and not isinstance(self.accelerator, TPUAccelerator):
            return self.strategy.is_distributed
        distributed_strategy = (
            DDPStrategy,
            DDPSpawnShardedStrategy,
            DDPShardedStrategy,
            DDPSpawnStrategy,
            DeepSpeedStrategy,
            XLAStrategy,
        )
        is_distributed = isinstance(self.strategy, distributed_strategy)
        if isinstance(self.accelerator, TPUAccelerator):
            is_distributed |= self.strategy.is_distributed
        return is_distributed
